/usr/include/linreg.h is in libalglib-dev 2.6.0-3.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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Copyright (c) 2007-2008, Sergey Bochkanov (ALGLIB project).
>>> SOURCE LICENSE >>>
This program is free software; you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation (www.fsf.org); either version 2 of the
License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
A copy of the GNU General Public License is available at
http://www.fsf.org/licensing/licenses
>>> END OF LICENSE >>>
*************************************************************************/
#ifndef _linreg_h
#define _linreg_h
#include "ap.h"
#include "ialglib.h"
#include "descriptivestatistics.h"
#include "gammafunc.h"
#include "normaldistr.h"
#include "igammaf.h"
#include "hblas.h"
#include "reflections.h"
#include "creflections.h"
#include "sblas.h"
#include "ablasf.h"
#include "ablas.h"
#include "ortfac.h"
#include "blas.h"
#include "rotations.h"
#include "bdsvd.h"
#include "svd.h"
struct linearmodel
{
ap::real_1d_array w;
};
/*************************************************************************
LRReport structure contains additional information about linear model:
* C - covariation matrix, array[0..NVars,0..NVars].
C[i,j] = Cov(A[i],A[j])
* RMSError - root mean square error on a training set
* AvgError - average error on a training set
* AvgRelError - average relative error on a training set (excluding
observations with zero function value).
* CVRMSError - leave-one-out cross-validation estimate of
generalization error. Calculated using fast algorithm
with O(NVars*NPoints) complexity.
* CVAvgError - cross-validation estimate of average error
* CVAvgRelError - cross-validation estimate of average relative error
All other fields of the structure are intended for internal use and should
not be used outside ALGLIB.
*************************************************************************/
struct lrreport
{
ap::real_2d_array c;
double rmserror;
double avgerror;
double avgrelerror;
double cvrmserror;
double cvavgerror;
double cvavgrelerror;
int ncvdefects;
ap::integer_1d_array cvdefects;
};
/*************************************************************************
Linear regression
Subroutine builds model:
Y = A(0)*X[0] + ... + A(N-1)*X[N-1] + A(N)
and model found in ALGLIB format, covariation matrix, training set errors
(rms, average, average relative) and leave-one-out cross-validation
estimate of the generalization error. CV estimate calculated using fast
algorithm with O(NPoints*NVars) complexity.
When covariation matrix is calculated standard deviations of function
values are assumed to be equal to RMS error on the training set.
INPUT PARAMETERS:
XY - training set, array [0..NPoints-1,0..NVars]:
* NVars columns - independent variables
* last column - dependent variable
NPoints - training set size, NPoints>NVars+1
NVars - number of independent variables
OUTPUT PARAMETERS:
Info - return code:
* -255, in case of unknown internal error
* -4, if internal SVD subroutine haven't converged
* -1, if incorrect parameters was passed (NPoints<NVars+2, NVars<1).
* 1, if subroutine successfully finished
LM - linear model in the ALGLIB format. Use subroutines of
this unit to work with the model.
AR - additional results
-- ALGLIB --
Copyright 02.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuild(const ap::real_2d_array& xy,
int npoints,
int nvars,
int& info,
linearmodel& lm,
lrreport& ar);
/*************************************************************************
Linear regression
Variant of LRBuild which uses vector of standatd deviations (errors in
function values).
INPUT PARAMETERS:
XY - training set, array [0..NPoints-1,0..NVars]:
* NVars columns - independent variables
* last column - dependent variable
S - standard deviations (errors in function values)
array[0..NPoints-1], S[i]>0.
NPoints - training set size, NPoints>NVars+1
NVars - number of independent variables
OUTPUT PARAMETERS:
Info - return code:
* -255, in case of unknown internal error
* -4, if internal SVD subroutine haven't converged
* -1, if incorrect parameters was passed (NPoints<NVars+2, NVars<1).
* -2, if S[I]<=0
* 1, if subroutine successfully finished
LM - linear model in the ALGLIB format. Use subroutines of
this unit to work with the model.
AR - additional results
-- ALGLIB --
Copyright 02.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuilds(const ap::real_2d_array& xy,
const ap::real_1d_array& s,
int npoints,
int nvars,
int& info,
linearmodel& lm,
lrreport& ar);
/*************************************************************************
Like LRBuildS, but builds model
Y = A(0)*X[0] + ... + A(N-1)*X[N-1]
i.e. with zero constant term.
-- ALGLIB --
Copyright 30.10.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuildzs(const ap::real_2d_array& xy,
const ap::real_1d_array& s,
int npoints,
int nvars,
int& info,
linearmodel& lm,
lrreport& ar);
/*************************************************************************
Like LRBuild but builds model
Y = A(0)*X[0] + ... + A(N-1)*X[N-1]
i.e. with zero constant term.
-- ALGLIB --
Copyright 30.10.2008 by Bochkanov Sergey
*************************************************************************/
void lrbuildz(const ap::real_2d_array& xy,
int npoints,
int nvars,
int& info,
linearmodel& lm,
lrreport& ar);
/*************************************************************************
Unpacks coefficients of linear model.
INPUT PARAMETERS:
LM - linear model in ALGLIB format
OUTPUT PARAMETERS:
V - coefficients, array[0..NVars]
NVars - number of independent variables (one less than number
of coefficients)
-- ALGLIB --
Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrunpack(const linearmodel& lm, ap::real_1d_array& v, int& nvars);
/*************************************************************************
"Packs" coefficients and creates linear model in ALGLIB format (LRUnpack
reversed).
INPUT PARAMETERS:
V - coefficients, array[0..NVars]
NVars - number of independent variables
OUTPUT PAREMETERS:
LM - linear model.
-- ALGLIB --
Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
void lrpack(const ap::real_1d_array& v, int nvars, linearmodel& lm);
/*************************************************************************
Procesing
INPUT PARAMETERS:
LM - linear model
X - input vector, array[0..NVars-1].
Result:
value of linear model regression estimate
-- ALGLIB --
Copyright 03.09.2008 by Bochkanov Sergey
*************************************************************************/
double lrprocess(const linearmodel& lm, const ap::real_1d_array& x);
/*************************************************************************
RMS error on the test set
INPUT PARAMETERS:
LM - linear model
XY - test set
NPoints - test set size
RESULT:
root mean square error.
-- ALGLIB --
Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lrrmserror(const linearmodel& lm,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
Average error on the test set
INPUT PARAMETERS:
LM - linear model
XY - test set
NPoints - test set size
RESULT:
average error.
-- ALGLIB --
Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lravgerror(const linearmodel& lm,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
RMS error on the test set
INPUT PARAMETERS:
LM - linear model
XY - test set
NPoints - test set size
RESULT:
average relative error.
-- ALGLIB --
Copyright 30.08.2008 by Bochkanov Sergey
*************************************************************************/
double lravgrelerror(const linearmodel& lm,
const ap::real_2d_array& xy,
int npoints);
/*************************************************************************
Copying of LinearModel strucure
INPUT PARAMETERS:
LM1 - original
OUTPUT PARAMETERS:
LM2 - copy
-- ALGLIB --
Copyright 15.03.2009 by Bochkanov Sergey
*************************************************************************/
void lrcopy(const linearmodel& lm1, linearmodel& lm2);
/*************************************************************************
Serialization of LinearModel strucure
INPUT PARAMETERS:
LM - original
OUTPUT PARAMETERS:
RA - array of real numbers which stores model,
array[0..RLen-1]
RLen - RA lenght
-- ALGLIB --
Copyright 15.03.2009 by Bochkanov Sergey
*************************************************************************/
void lrserialize(const linearmodel& lm, ap::real_1d_array& ra, int& rlen);
/*************************************************************************
Unserialization of DecisionForest strucure
INPUT PARAMETERS:
RA - real array which stores decision forest
OUTPUT PARAMETERS:
LM - unserialized structure
-- ALGLIB --
Copyright 15.03.2009 by Bochkanov Sergey
*************************************************************************/
void lrunserialize(const ap::real_1d_array& ra, linearmodel& lm);
void lrlines(const ap::real_2d_array& xy,
const ap::real_1d_array& s,
int n,
int& info,
double& a,
double& b,
double& vara,
double& varb,
double& covab,
double& corrab,
double& p);
void lrline(const ap::real_2d_array& xy,
int n,
int& info,
double& a,
double& b);
#endif
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